from __future__ import print_function
from keras.models import Model
from keras.layers import Input, LSTM, Dense
import numpy as np 

batch_size = 64
epochs = 100
latent_dim = 256
num_samples = 10000
data_path = 'D:/work/tensorflow/study/cmn-eng/cmn.txt'

input_texts = []
target_texts = []
input_characters = set()
target_characters = set()

with open(data_path, 'r', encoding = 'utf-8') as f:
	lines = f.read().split('\n')

for line in lines[: min(num_samples, len(lines) - 1)]:
	input_text, target_text = line.split('\t')
	target_text = '\t' + target_text + '\n'
	input_texts.append(input_text)
	target_texts.append(target_text)
	for char in input_text:
		if char not in input_characters:
			input_characters.add(char)
	for char in target_text:
		if char not in target_characters:
			target_characters.add(char)

input_characters = sorted(list(input_characters))
target_characters = sorted(list(target_characters))
num_encoder_tokens = len(input_characters)
num_decoder_tokens = len(target_characters)
max_encoder_seq_length = max([len(txt) for txt in input_texts])
max_decoder_seq_length = max([len(txt) for txt in target_texts])

print('Number of samples: ', len(input_texts))
print('Number of unique input tokens: ', num_encoder_tokens)
print('Number of unique output tokens: ', num_decoder_tokens)
print('Max sequence length for inputs: ', max_encoder_seq_length)
print('Max sequence length for outputs: ', max_decoder_seq_length)

input_token_index = dict([(char, i) for i, char in enumerate(input_characters)])
target_token_index = dict([(char, i) for i, char in enumerate(target_characters)])
encoder_input_data = np.zeros((len(input_texts), max_encoder_seq_length, num_encoder_tokens), dtype = "float32")
decoder_input_data = np.zeros((len(input_texts), max_decoder_seq_length, num_decoder_tokens), dtype = "float32")
decoder_target_data = np.zeros((len(input_texts), max_decoder_seq_length, num_decoder_tokens), dtype = "float32")

for i, (input_text, target_text) in enumerate(zip(input_texts, target_texts)):
	for t, char in enumerate(input_text):
		encoder_input_data[i, t, input_token_index[char]] = 1.
	for t, char in enumerate(target_text):
		decoder_input_data[i, t, target_token_index[char]] = 1.
		if t > 0:
			decoder_target_data[i, t - 1, target_token_index[char]] = 1.

encoder_inputs = Input(shape = (None, num_encoder_tokens))
encoder = LSTM(latent_dim, return_state = True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
encoder_states = [state_h, state_c]

decoder_inputs = Input(shape = (None, num_decoder_tokens))
decoder_lstm = LSTM(latent_dim, return_sequences = True, return_state = True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state = encoder_states)
decoder_dense = Dense(num_decoder_tokens, activation = 'softmax')
decoder_outputs = decoder_dense(decoder_outputs)

model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(optimizer = 'rmsprop', loss = 'categorical_crossentropy')
model.fit([encoder_input_data, decoder_input_data], decoder_target_data, batch_size = batch_size, epochs = epochs, validation_split = 0.2)
model.save('s2s.h5')

encoder_model = Model(encoder_inputs, encoder_states)

decoder_state_input_h = Input(shape = (latent_dim,))
decoder_state_input_c = Input(shape = (latent_dim,))
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
decoder_outputs, state_h, state_c = decoder_lstm(decoder_inputs, initial_state = decoder_states_inputs)
decoder_states = [state_h, state_c]
decoder_outputs = decoder_dense(decoder_outputs)
decoder_model = Model([decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states)

reverse_input_char_index = dict((i, char) for char, i in input_token_index.items())
reverse_target_char_index = dict((i, char) for char, i in target_token_index.items())

def decode_sequence(input_seq):

	states_value = encoder_model.predict(input_seq)
	target_seq = np.zeros((1, 1, num_decoder_tokens))
	target_seq[0, 0, target_token_index['\t']] = 1

	stop_condition = False
	decoded_sentence = ''
	while not stop_condition:
		output_tokens, h, c = decoder_model.predict([target_seq] + states_value)
		sampled_token_index = np.argmax(output_tokens[0, -1, :])
		sampled_char = reverse_target_char_index[sampled_token_index]
		decoded_sentence += sampled_char

		if (sampled_char == '\n' or len(decoded_sentence > max_decoder_seq_length)):
			target_seq = np.zeros((1, 1, num_decoder_tokens))
			target_seq[0, 0, sampled_token_index] = 1.
			states_value = [h, c]

	return decoded_sentence

for seq_index in range(100):
	input_seq = encoder_input_data[seq_index: seq_index + 1]
	decoded_sentence = decode_sequence(input_seq)
	print('-')
	print('Input sentence: ', input_texts[seq_index])
	print('Decoded sentence: ', decoded_sentence)